DataRobot-Research/drdocs huggingface dataset:
---
title: Fraudulent claim detection
description: Improve the accuracy in predicting which insurance claims are fraudulent.
---

# Fraudulent claim detection {: #fraudulent-claim-detection }

This page outlines the use case to improve the accuracy in predicting which insurance claims are fraudulent. It is captured below as a UI-based walkthrough. It is also available as a [Jupyter notebook](pred-fraud-v3.ipynb) that you can download and execute.

{% include 'includes/fraud-claims-include.md' %}

### No-Code AI Apps {: #no-code-ai-apps }

Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. Once the model is deployed, predictions can be consumed for use in the [decision process](#decision-process). For example, this [No-Code AI App](app-builder/index) is an easily shareable, AI-powered application using a no-code interface:

![](images/fraud-claim-1.png)

![](images/fraud-claim-2.png)

### Notebook demo {:#notebook-demo}

See the notebook version of this accelerator [here](pred-fraud-v3.ipynb).
# Presto {: #presto }

## Supported authentication {: #supported-authentication }

- Username/password

## Prerequisites {: #prerequisites }

The following is required before connecting to Presto in DataRobot:

- Presto account

## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with Presto:

Required field | Description | Documentation
--------------- | ---------- |  -----------
`address`       | The connection URL that supplies connection information for Presto.  |    [Presto documentation](https://prestodb.io/docs/current/installation/jdbc.html#connecting){ target=_blank }

Learn about additional [configuration options for Presto](https://prestodb.io/docs/current/installation/jdbc.html#connecting){ target=_blank }.

{% include 'includes/data-conn-trouble.md' %}
---
title: Workbench experimentation
description: Get started with DataRobot's value-driven AI. Analyze data, create models, and leverage code-first accelerators and notebooks.

---

# Workbench experimentation {: #workbench-experimentation }

Get started with DataRobot's Workbench experience. Analyze data, create models, and leverage code-first accelerators and notebooks.

Topic          | Describes...
---------------|---------------
[Workbench capabilities](gs-wb-capabilities)    | Compare the evolving capabilities of Workbench with the DataRobot Classic experience.
[Fundamentals of Workbench](gs-wb-fundamentals) | Understand the components of the DataRobot Workbench interface, including the architecture, some sample workflows, and directory landing page.
[Work with data](gs-wb-data)           | Use DataRobot's integrated capabilities to import, explore, and prepare your data for modeling.
[Build experiments](gs-wb-experiments) | Data in, models out&mdash;click to model and immediately start investigating model insights.
# Version 7.3.3 {: #version-733 }

_January 17, 2022_

The DataRobot v7.3.3 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:

* [Features introduced in v7.3.0](v7.3.0-aml)

## Issues fixed in v7.3.3 {: #issues-fixed-in-v733 }

The following issues have been fixed since Enterprise release v7.3.2:

### Platform {: #platform }

* PLT-5635: Fixes an issue affecting LDAP group mapping when an update has been made to the system-level group’s settings and/or permissions post creation.
* PLT-5365: Fixes an issue where EMC ECS S3-compatible storage integration would give "SSL validation failed."

### Feature Discovery {: #feature-discovery }

* SAFER-4504: Fixes an issue where relationships could not be saved for some JDBC datasets in Feature Discovery projects.

### Predictions {: #predictions }

* CODEGEN-1253: Replaces log4j in Scoring Code JARs with sl4fj-simple.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
# Version 8.0.4 {: #version-804 }

_June 29, 2022_

The DataRobot v8.0.4 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

## Issues fixed in v8.0.4 {: #issues-fixed-in-v804 }

The following issues have been fixed since release v8.0.3:

### Enterprise {: #enterprise }

* EPS-451: The `eks-custom-tasks.yaml`, `eks-mlops.yaml`, and `multi-node-mlops.yaml` example configuration files no longer use `journald` as the `docker_log_driver`.

#### Platform {: #platform }

* DM-5972: Restricts list schemas in BigQuery JDBC connections to only return items from the current catalog or project, speeding up list tables when the schema is specified and the catalog is not.

* DM-6784: The endpoint `GET /externalDataStore/` now returns data connection details with only OAuth fields filtered out.

* DM-6789: Ensures that list tables properly restrict listed tables based on the specified project ID for BigQuery data connections.

* DM-6791: Fixes an issue causing infinite loading in BigQuery data connection schemas.

* PLT-6649: Fixes an issue where the user is returned to their identity provider after logging out if `SKIP_LOGIN_UI_IF_SAML_SSO_IS_ENFORCED=True`, causing the user to be automatically signed back in instead of remaining on the DataRobot log in page.

#### Predictions {: #predictions }

* PRED-7724: Fixes an issue when creating batch prediction jobs without the organization's identification.

* PRED-7750: `predictionscompute` and `predictionsccomputerpy2` now successfully authenticate using Kerberos when HDFS is used for file storage.

#### Time series {: #time-series }

* TIME-11049: Fixes an issue where the identified time series properties like stationarity, exponential trend, and seasonality can differ between time series projects using the same dataset when the subset of the longest series in the dataset is larger than 10 series.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
# Version 8.0.7 {: #version-807 }

_September 30, 2022_

The DataRobot v8.0.7 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

In addition to the issues listed below, DataRobot addressed several outstanding security issues. This release/update is part of DataRobot's continual effort to secure the product and platform. {% if 'enterprise' in tags %}See also:

* [Deprecation notice](#deprecation-announcement)

{% endif %}

## Issues fixed in v8.0.7 {: #issues-fixed-in-v807 }

The following issues have been fixed since release v8.0.6:

### Data management {: #data-management }

- DM-7142 and DM-7143: Upgrades the underlying ElasticSearch engine in the AI Catalog to 7.17.4.

### MLOps {: #mlops }

- MMM-10127: Improves the performance of challenger models replay for deployments with a long history.
- MMM-10621: Fixes an issue where model packages from the same DataRobot installation would be reported as coming from different installations.
- MMM-10797: Fixes an issue causing incorrect computations of thresholded accuracy metrics for MLOps.
- RAPTOR-8567: Fixes an issue when running insights on custom inference models without internet access.

### Predictions {: #predictions }

- PRED-7854: Retires DataRobot's bundled Spark 2.4.5 in favor of the system-provided Spark on CDH 5 and CDH 6.

### Time series {: #time-series }

- TIME-11677: Skips sorting during modeling when loading series in projects with less than 1000 series.
- TIME-11684: Fixes an issue with Predictions Explanation computation in forecast range predictions from a time series deployment.
- TIME-11782: Fixes an issue with feature derivation for multiseries datasets that have a multiseries id column named `index`.

{% if 'enterprise' in tags %}

## Deprecation announcement {: #deprecation-announcement }

### Custom tasks removed {: #custom-tasks-removed }

The initial release of 8.0 included a preview implementation of the custom tasks feature. With this release, DataRobot is removing the preview feature and will replace it in an upcoming release.

{% endif %}


_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: DataRobot Notebooks
description: Read preliminary documentation for DataRobot's notebook features currently in the DataRobot public preview pipeline.
section_name: Notebooks
---

# DataRobot Notebooks {: #datarobot-notebooks }

{% include 'includes/notebooks/nb-index-main.md' %}

## Browser compatibility {: #browser-compatibility }

{% include 'includes/browser-compatibility.md' %}
---
title: Feature selection notebooks
description: Review notebooks that outline feature selection.

---

# Feature selection notebooks {: #feature-selection-notebooks }

DataRobot offers end-to-end code examples via Jupyter notebooks that help you find complete examples of common data science and machine learning workflows. Review the notebooks that outline feature selection below.

Topic | Describes... |
----- | ------ |
[Feature Importance Rank Ensembling](feat-select/Feature-Importance-Rank-Ensembling.ipynb) | Learn about the benefits of Feature Importance Rank Ensembling (FIRE)&mdash;a method of advanced feature selection that uses a median rank aggregation of feature impacts across several models created during a run of Autopilot. |
[Advanced feature selection with Python](python-select.ipynb) | Use Python to select features by creating aggregated Feature Impact. |
---
title: Custom charts
description: The ROC Curve tab lets you create custom charts that help you explore classification, performance, and statistics related to a selected machine learning model.

---

# Custom charts {: #custom-charts }

The Chart pane in the **[ROC Curve](roc-curve-tab-use)** tab allows you to create your own charts to explore classification, performance, and statistics related to a selected model.

## Create a custom chart {: #create-a-custom-chart }

Create a custom chart by selecting values for the X- and Y- axes:

1. In the **Chart** pane, select **Custom Chart**.

    ![](images/roc-pre-select-custom-chart.png)

2. In the **X-Axis** dropdown list, select the value to display on the X-Axis. Do the same for the **Y-Axis** and click **Apply**.

    ![](images/roc-pre-custom-chart-select-xy.png)

    The custom chart displays in the Chart pane.

    ![](images/roc-pre-select-custom-chart-results.png)

    Hover over the circle on the graph to see the values at the display threshold.

## Data available for custom charts {: #data-available-for-custom-charts }

Click below to view the data available for custom charts.

=== "X-axis values"

    * False Positive Rate (Fallout)
    * True Positive Rate (Sensitivity)
    * True Negative Rate (Specificity)
    * Fraction Predicted as Positive
    * Fraction Predicted as Negative
    * Threshold (Probability)

=== "Y-axis values"

    * False Positive Rate (Fallout)
    * True Positive Rate (Sensitivity)
    * True Negative Rate (Specificity)
    * Cumulative List (Positive)
    * Cumulative List (Negative)
    * Fraction Predicted as Positive
    * Fraction Predicted as Negative
    * Profit (Overall)
    * Profit (Average)
    * Threshold (Probability)
    * F1 Score
    * Negative Predictive Value
    * Positive Predictive Value
    * Accuracy
    * Matthews Correlation Coefficient
With the **Comments** link, you can add comments to&mdash;even host a discussion around&mdash;any item in the catalog that you have access to. Comment functionality is available in the **AI Catalog** (illustrated below), and also as a model tab from the Leaderboard and in use case tracking. With comments you can:

* Tag other users in a comment; DataRobot will then send them an email notification.
* Edit or delete any comment you have added (you cannot edit or delete other users' comments).

![](images/catalog-26.png)
---
title: Accuracy Over Space
description: Location AI insights help to discover spatial patterns in prediction errors and visualize prediction errors across data partitions on a map visualization.
---

# Accuracy Over Space {: #accuracy-over-space }

To assess model fidelity in a spatial setting, Location AI adds powerful model evaluation tools to DataRobot. Location AI insights help to discover spatial patterns in prediction errors and visualize prediction errors across data partitions on a map visualization. Location AI facilitates these insights through the **Evaluate > Accuracy Over Space** tab for an individual model.

The **Accuracy Over Space** tab provides a spatial residual mapping within an individual model. It provides similar visualizations to Location AI [ESDA](lai-esda), but allows you to explore prediction error metrics across all data partitions.

![](images/lai-11.png)

By default, **Accuracy Over Space** displays residual (prediction error) values on a unique map based on the validation partition. Co-located points are displayed using the average value of all points at that location. The visualization settings for the map can be adjusted in the same manner as Location AI ESDA visualizations. Additional settings for the tool include:

* Data Selection: Sets which data partition to visualize, either validation, cross-validation, or holdout.

* Metric Type: Sets the value to report at each location, either Residual, Actual, or Predicted.

* Aggregation: Sets the arithmetic to use for co-located locations, either Avg, Min, Max, Count, or Value.

	* Count reports the count of each geometry in the dataset. It reports the sum of counts for a hexagon or grid map of a geometry feature.

	* Value: When all geometries have a count of 1, displays the feature value (no aggregation).

	![](images/lai-14.png)


**Accuracy Over Space** also supports different map visualizations: [Kernel density map](lai-esda#kernel-density-map), [Hexagon map](lai-esda#hexagon-map), and [Heatmap](lai-esda#heat-map).
---
title: Bias and fairness
description: Introduces the Bias and Fairness tabs, which identify if a model is biased and why the model is learning bias from the training data.

---

# Bias and Fairness {: #bias-and-fairness }

The **Bias and Fairness** tabs identify if a model is biased and why the model is learning bias from the training data. The following sections provide additional information on using the tabs:

Leaderboard tab  | Description | Source
------------------|-------------|------------
[Per-Class Bias](per-class)  | Identify if a model is biased, and if so, how much and who it's biased towards or against. | Validation data
[Cross-Class Data Disparity](cross-data) | Depict why a model is biased, and where in the training data it learned that bias from. | Validation data
[Cross-Class Accuracy](cross-acc)  | Measure the model's accuracy for each class segment of the protected feature. | Validation data
[Settings](fairness-metrics#configure-metrics-and-mitigation-post-autopilot) | Configure fairness tests from the Leaderboard. | N/A

If you did not configure **Bias and Fairness** prior to model building, you can [configure fairness tests for Leaderboard models](fairness-metrics#configure-metrics-and-mitigation-post-autopilot) in **Bias and Fairness** > **Settings**.

See the [Bias and Fairness reference](bias-ref) for a description of the methods used to calculate fairness for a machine learning model and to identify any biases from the model's predictive behavior.

##  Bias and Fairness considerations {: #bias-and-fairness-considerations }

Consider the following when using the **Bias and Fairness** tab:

- Bias and fairness testing is only available for binary classification projects.
- Protected features must be categorical features in the dataset.
# Version 7.3.2 {: #version-732 }

_December 21, 2021_

The DataRobot v7.3.2 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:

* [Features introduced in v7.3.0](v7.3.0-aml)

## Issues fixed in v7.3.2 {: #issues-fixed-in-v732 }

The following issues have been fixed since Enterprise release v7.3.1:

### Business Operations {: #business-operations }

* BOPS-2533: Fixes an issue affecting the distribution chart for regression models on the prediction result page in the AI App Builder.
* BOPS-2576: Fixes an issue causing created applications to not display on the **Applications** page.

### MLOps {: #mlops }

* MMM-8649: Fixes issue with deployment reports failing for clusters running with `PYTHON3_SERVICES` enabled.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
# Version 7.3.5 {: #version-735 }

_February 18, 2022_

The DataRobot v7.3.5 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.3.0 release notes for:

* [Features introduced in v7.3.0](v7.3.0-aml)

## Updated language localization {: #updated-language-localization }

Localization of the documentation has been updated to include Japanese content for all new 7.3 features.

To change languages, open your [user settings](getting-help) and select a language for your session. The selection remains until you reset it.

![](images/languages-set-pt-jp.png)

## Issues fixed in v7.3.5 {: #issues-fixed-in-v735 }

The following issues have been fixed since Enterprise release v7.3.4:

### Feature Discovery {: #feature-discovery }

* SAFER-4717: Fixes an issue causing an OOM error for batch prediction jobs in Feature Discovery projects with Snowflake secondary datasets configured.

### Predictions {: #predictions }

* PRED-7092: Fixes an issue with duplicated headers in output prediction files from AI Catalog intake.
* PRED-7028: BigQuery output adapter in batch predictions now uses the existing table schema in case of insert.

### Time series {: #time-series }

* TIME-10099: Fixes an issue with weights and early stopping in multiclass projects. Only LightGBM- and GBM-based models were affected.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Notification center
---

# Notification center {: #notification-center }

The alert icon (![](images/icon-alert.png)) provides access to notifications sent from the DataRobot platform. A numeric indicator on top of the alert icon indicates that you have unread notifications.

![](images/note-alert.png)

Click the icon to see a list of notifications. Note that once you click on the icon, the indicator disappears. To remove a notification, hover on it and click the trash can icon ![](images/icon-delete.png). If you do not delete them, the notification center lists up to the last 100 events. Notifications expire and are removed after one month.

![](images/note-alert-1.png)

Notifications are delivered for the following events. Click the notification to open the project or the model in the deployment area that is related to the event.

| Event name | Description |
|-----------------------------|------------------------|
|  Autopilot has finished | Reports that Autopilot—either Quick or full mode—has completed. |
| Project shared | Reports that a project has been shared with you. DataRobot also delivers an email notification with a link to the project. |
| New comment | Alerts that a comment has been added to a project you own, and displays the comment. |
| New mention | Reports that you have been mentioned in a project. |
| Data drift detected | Indicates that a deployed model has experienced [data drift](data-drift) with a status of failing (red). |
|  Deployment is unhealthy | Indicates that [service health](service-health) for a deployed model—its ability to respond to prediction requests quickly and reliably—has severely declined since the model was deployed.  |
|  Deployed model accuracy decreased  |  Indicates that model [accuracy](deploy-accuracy) for a deployed model has severely declined since the model was deployed. |
# Version 7.2.5 {: #version-725 }

_November 9, 2021_

The DataRobot v7.2.5 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:

* [Features introduced in v7.2.0](v7.2.0-aml)

## Issues fixed in v7.2.5 {: #issues-fixed-in-v725 }

The following issues have been fixed since Enterprise release v7.2.3:

### Enterprise {: #enterprise }

* EP-1944: Adds support for OpenSSL FIPS.
* EP-1965: Updates Patroni to use md5 as password encryption for backward compatibility.

### Platform {: #platform }

* PLT-4930: Moves the `ENABLE_USERS_PERMA_DELETE_API` feature flag to the Public Preview section in Settings.
* PLT-5079: Fixes an issue in the Azure storage backend that was affecting the speed prediction data processing for external deployments.

### Trust and Explainability {: #trust-and-explainability }

* TREX-397: Allows Unicode characters to be used in Bias and Fairness target names.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Mastering tables in production ML
description: Review an AI accelerator that uses a repeatable framework for a production pipeline from multiple tables.

---

# Mastering tables in production ML {: #mastering-tables-in-production-ml }

[Access this AI accelerator on GitHub <span style="vertical-align: sub">:material-arrow-right-circle:{.lg }</span>](https://github.com/datarobot-community/ai-accelerators/tree/main/advanced-experimentation/AFD){ .md-button }

We've all been there: data for customer transactions are in one table, but the customer membership history is in another. Or, you have sensor-level data at the sub-second level in one table, machine errors in another table, and production demand in yet another table, all at different time frequencies.  Electronic Medical Records (EMRs) are another common instance of this challenge. You have a use case for your business you want to explore, so you build a v0 dataset and use simple aggregations from before, perhaps in a feature store.  But moving past v0 is hard.

The reality is, the hypothesis space of relevant features explodes when considering multiple data sources with multiple data types in them. By dynamically exploring the feature space across tables, you minimize the risk of missing signal by feature omission and further reduce the burden of a priori knowledge of all possible relevant features.

Event-based data is present in every vertical and is becoming more ubiquitous across industries. Building the right features can drastically improve performance. However, understanding which joins and time horizons are best suited to your data is challenging, and also time-consuming and error-prone to explore.

In this accelerator, you'll find a repeatable framework for a production pipeline from multiple tables. This code uses Snowflake as a data source, but it can be extended to any supported database. Specifically, the accelerator provides a template to:

* Build time-aware features across multiple historical time-windows and datasets using DataRobot and multiple tables in Snowflake (or any database).
* Build and evaluate multiple feature engineering approaches and algorithms for all data types.
* Extract insights and identify the best feature engineering and modeling pipeline.
* Test predictions locally.
* Deploy the best-performing model and all data preprocessing/feature engineering in a Docker container, and expose a REST API.
* Score from Snowflake and write predictions back to Snowflake.